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학술지 Fuzzy Vector Signature and Its Application to Privacy-Preserving Authentication
Cited 5 time in scopus Download 6 time Share share facebook twitter linkedin kakaostory
저자
서민혜, 황정연, 이동훈, 김수형, 김승현, 박종환
발행일
201905
출처
IEEE Access, v.7, pp.69892-69906
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2019.2919351
협약과제
19HH5400, O2O 서비스를 위한 무자각 증강인증 및 프라이버시가 보장되는 블록체인 ID 관리 기술 개발, 김수형
초록
Fuzzy authentication uses non-deterministic or noisy data, like biometrics, as an authentication factor. Although the data is extracted from the same individual or source, it can be different for each measurement. As a result, one of the main issues in fuzzy authentication is the effective processing of the fuzziness, while guaranteeing the privacy of the fuzzy data. Biometric data is a typical user-generated fuzzy data and the fuzzy extractor is one of the most promising primitives for biometric authentication these days. In 2016, Canetti et al. proposed the reusable fuzzy extractor, in which multiple keys can be generated with the same biometric data. It can also handle some outliers which occur unexpectedly (owing to an external interference when acquiring the fuzzy data, for example, the presence of dust on a fingerprint image). However, the size of the user's helper data in the reusable fuzzy extractor is quite large. This makes the network bandwidth usage required in the online authentication phase (or the storage required on the user side) considerable, which inconveniences the user. In this paper, we present a new primitive for fuzzy authentication, called a fuzzy vector signature (FVS) scheme, which significantly alleviates the burden on the user side. This means that the network bandwidth usage (or the amount of storage required on the user side) is significantly reduced. The proposed FVS scheme is reusable and robust to outliers as well. Finally, we provide a privacy-preserving fuzzy authentication protocol based on the FVS scheme.
KSP 제안 키워드
Biometric authentication, External interference, Fingerprint image, Fuzzy data, Fuzzy extractor, Fuzzy vector, Helper data, Network bandwidth, Noisy data, Robust to outliers, User-generated